Promoting positive activity patterns

ABSTRACT

Methods, computer systems, and computer readable media are provided for promoting positive activity patterns for users and facilitate long-term adherence to the activity patterns, such as by providing alerts or electronic reminders to ambulate in a fashion that is responsive to an individual&#39;s actual activity patterns and behaviors and compatible with routine activities in the workplace and home. In particular, embodiments of the present invention are directed to (1) measuring physical activity patterns during the waking hours of a human, and in some embodiments continuously measuring these activity patterns; (2) automatically ascertaining whether the patterns exhibit sufficient frequency and variability of activity such as confers certain health benefits; and (3) if the patterns do not manifest such features, to adaptively provide sensible reminders at irregular within-day intervals such as are likely to establish healthy patterns of ambulation and other light activity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/751,961, titled “Promoting Positive Activity Patterns”, filed Jan.28, 2013, which claims the benefit of U.S. Provisional Application No.61/591,515, titled “Randomized Reminding For Promoting Positive ActivityPatterns,” filed Jan. 27, 2012, both of which are hereby expresslyincorporated by reference in their entireties.

INTRODUCTION

A major goal of industrialized Western societies is to reduce theprevalence of overweight and obesity and inter-related comorbid chronichealth conditions. The burden of diabetes is increasing worldwide,indicating an urgent need to find the best standard for diabetes care.There is a close relationship between obesity and Type 2 diabetes: 90%of individuals with diabetes are overweight or obese. The basictreatment is weight loss, physical activity, and diet. However, researchindicates that only a very small portion of weight lost is directlyattributable to associated caloric expenditures, which tend to beespecially low in obese and deconditioned individuals initiatingexercise.

Exercise is a type of physical activity that is planned and structured.But there are non-exercise, unplanned forms of activity as well, such asintermittent walking and stair-climbing that are incidental to otherdaily activities, intermittent rising and sitting down, intermittentstretching, and so forth. It is proposed that all activity-inducedchanges in dynamic gene expression and neuroendocrine physiologycontribute to the statistical association between physical activity,psychological state, metabolic state, and weight loss. It is possiblethat exercise- and non-exercise activity-induced improvements inadherence to exercise prescriptions are related to increased feelings ofability to control one's own behavior. Biochemical theories suggest thatassociated changes in endorphin, serotonin, and norepinephrine levelsinduce improvements; thus, there would be a dose-response effect (i.e.,more exercise—more reduction in depression, more adherence, etc.).Conversely, behavioral theories suggest that simply participating in aprogram of physical activity fosters self-efficacy to manage one's selfthrough barriers that lead to a generalized sense of well-being andimproved adherence. Studies have supported both positions.

Many social and psychological barriers need to be overcome to achieveweight loss, fitness, and prevent Type 2 diabetes and otherlifestyle-related chronic conditions. Health care professionals'attitudes recently have shifted from compliance-seeking and traditional,paternalistic physician-led care to a model in which all are “teammembers.” The individuals who are the subject of the care themselvesplay an active role, resulting in increased empowerment. In that regard,one of the main factors in preventing and improving outcomes in obesity,diabetes, hypertension, osteoporosis and other chronic conditions issecuring the individual's active participation in self-care and caredecisions.

Coaching communication and reflection play an important role as well.The most reliable indicator for success is the individuals' internalmotivation. This is consistent with studies that have found that theindividual's willingness to take responsibility for lifestyle changeswas important to success, and enhancing the individual's motivationthrough coaching was perceived as one of the health care professionals'most important duties.

Contemporary daily activity recommendations for fitness and weight lossare primarily concerned with exercise intensity (100 steps/min speedrecommended—3.0 METs), repetition counts (3 or more bouts of at least1,000 steps each), activity duration (at least 10 min per bout), andbasal level of activity (greater than 5,000 steps during the rest ofday, totaling 10,000 steps or more). There is nothing inherently “wrong”with these, but by and large, these simple recommendations arerationalized in terms of their amenability to individuals' rememberingthem and “checking them off” their to-do list each day. Regimens thatentail numerous or varied prescribed activities are rightly regarded astoo complex or cumbersome for ordinary people to remember or adhere to.However, the simple recommendations are founded on an overly simplisticconcept of energy intake and expenditure. Non-exercise activitythermogenesis (NEAT) from standing or other non-sedentary behaviors isneglected. Energy-dissipating neuroendocrine correlates of elevatedlevels of attention and arousal associated with intermittent walking andpostural changes are also ignored by the recommendations.

Furthermore, the standard daily activity recommendations' simplicity isfounded on anachronistic notions of people's need to spontaneouslyremember to do things—in the fashion that prevailed 20 years ago ormore. Today, however, nearly every person carries a mobile phone, oftencontaining sensors and sufficient computational power to implementelectronic music libraries, and stock-trading applications, videoplayback, digital camera, and myriad other sorts of software. Sportswatches and other electronic wearable devices today similarly containprodigious computational power and sensors, far beyond what is requiredfor the nominal use-case for which the devices were originally designed.There are presently more than 1,500 health-related software “apps”available from iTunes and Android markets for mobile devices.

It is therefore practical today to create small, wearable devices thatcan monitor and intelligently analyze patterns of movement that aredetected by sensors in such devices, enabling highly varied, complex,and context-aware personalized guidances, as some embodiments of theinvention provide, thereby obviating any need for the user to commit any“regimen” to memory.

Evidence suggests that health authorities' recommended goal of 10,000steps/day may not be sustainable for some groups, including older adultsand those living with chronic diseases. Another concern about using theconventional recommendation of 10,000 steps/day as a universal step goalis that it is probably too low for children, an important targetpopulation in the war against obesity. Alternative approaches topedometer-determined physical activity recommendations that are recentlyshowing promise of health benefit and individual sustainability havebeen based on incremental improvements relative to baseline values.However, most such alternatives continue to be highly simplistic andfocus solely on energy balance.

Predominantly as a result of several decades' changes, many professionaland service jobs have become progressively more and more sedentary, manypeople's non-exercise activity level has dropped to very low levels,such that obesity and other health problems have reached epidemicproportions. Accordingly an aim of embodiments of the present inventionis to automatically provide alerts or electronic reminders to ambulate,in a fashion that is (a) responsive to an individual's actual activitypatterns and behaviors and (b) compatible with routine activities in theworkplace and home, so as to facilitate long-term adherence.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The present invention is defined by the claims.

Systems, methods, and computer-readable media are provided for promotingpositive activity patterns for users and facilitate long-term adherenceto the activity patterns, such as by providing alerts or electronicreminders to ambulate in a fashion that is responsive to an individual'sactual activity patterns and behaviors and compatible with routineactivities in the workplace and home. In particular, embodiments of thepresent invention are directed to (1) measuring physical activitypatterns during the waking hours of a human, and in some embodimentscontinuously measuring these activity patterns; (2) automaticallyascertaining whether the patterns exhibit sufficient frequency andvariability of activity such as confers certain health benefits; and (3)if the patterns do not manifest such features, to adaptively providesensible reminders at irregular within-day intervals such as are likelyto establish healthy patterns of ambulation and other light activity.

For example, in one aspect, embodiments of a method of producingentropically-driven reminders includes receiving user-motion informationover a time interval for each of a plurality of time intervals spanninga first time period. For a first time interval from the plurality oftime intervals, the method determines a time series based on user-motioninformation received during the first time interval and also based onuser-motion information received during each time interval of theplurality of time intervals, which occurred prior to the first timeinterval. Embodiments of method further comprise determining an amountof activity variability, such as the entropy, for the first timeinterval, based on the time series and comparing the amount of activityvariability to a target threshold. Embodiments of method furthercomprise generating a pseudorandom value if the amount of activityvariability is less than the target threshold, and based on thegenerated pseudorandom value, which in some embodiments representslogical true or false, determining to provide a notice. In embodiments,a notice is provided if the pseudorandom value represents logical true,and a not provided if the pseudorandom value represents logical false.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are described in detail below with reference to the attacheddrawing figures, wherein:

FIG. 1A depicts a block diagram of an illustrative operating environmentsuitable to implement embodiments of the invention;

FIG. 1B depicts a block diagram of an illustrative operating environmentsuitable for practicing an embodiment of the invention;

FIG. 1C depicts aspects of an illustrative operating environmentsuitable for practicing an embodiment of the invention; and

FIG. 2 depicts a flow diagram of a method for promoting positiveactivity patterns for users and facilitating long-term adherence to theactivity patterns, by providing entropically-driven activity reminders,in accordance with an embodiment of the invention.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventor has contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

As one skilled in the art will appreciate, embodiments of our inventionmay be embodied as, among other things: a method, system, or set ofinstructions embodied on one or more computer-readable media.Accordingly, the embodiments may take the form of a hardware embodiment,a software embodiment, or an embodiment combining software and hardware.In one embodiment, the invention takes the form of a computer-programproduct that includes computer-usable instructions embodied on one ormore computer-readable media.

Computer-readable media include both volatile and nonvolatile media,removable nonremovable media, and contemplate media readable by adatabase, a switch, and various other network devices. By way ofexample, and not limitation, computer-readable media comprise mediaimplemented in any method or technology for storing information,including computer-storage media and communications media. Examples ofstored information include computer-useable instructions, datastructures, program modules, and other data representations. Computerstorage media examples include, but are not limited toinformation-delivery media, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile discs (DVD), holographicmedia or other optical disc storage, magnetic cassettes, magnetic tape,magnetic disk storage, other magnetic storage devices, and other storagedevices. These technologies can store data momentarily, temporarily, orpermanently.

Embodiments of the invention are directed to methods, computer systems,and computer-readable media for promoting positive activity patterns forusers and/or facilitating long-term adherence to the activity patterns,such as by providing alerts or electronic reminders to ambulate in afashion that is responsive to an individual's actual activity patternsand behaviors and compatible with routine activities in the workplaceand home. Compared to existing methodologies whose reminders are notadaptive, are emitted with precise recurring periodicity, and quicklybecome annoying, embodiments of the present invention with contingent,aperiodic, pattern-triggered health-promoting reminding anddecision-support action is sufficiently responsive and adaptive to theevolving active and sedentary behaviors of the user so as to mimic theguidance of a health instructor or personal coach. The efficacy of—andsustained adherence to—entropically-guided activity reminders areconsequently very high, and the system and method for producingentropically-driven reminders are therefore valuable for preventing ormanaging conditions such as obesity, osteoporosis, diabetes, andhypertension.

A further aim of embodiments of the invention include establishing andsupporting specific temporal patterns of frequent, aperiodic,high-entropy activity as archaeologists and anthropologists believecharacterized primitive “hunter-gatherer” cultures for whose members“modern” chronic diseases like obesity, cardiovascular disease, diabetesand other conditions were substantially absent. As such,entropically-guided physical activity promotion serves as a usefuladjunct to—but does not replace—other existing state of the artrecommendations, including “10,000 steps” and “30-min bout ofmoderate-to-strenuous exercise at least 5 times per week.” Theincremental value of entropically-guided activity reminding isparticularly significant for individuals of working age who are employedin highly sedentary occupations, who may experience great difficultyadhering to recommendations provided by the existing state of the art.

Turning now to FIGS. 1A-1C, aspects of illustrative operatingenvironments suitable for use in implementing embodiments of the presentinvention are provided. FIG. 1A depicts a block diagram of anillustrative example operating environment with which embodiments of thepresent invention may be implemented designated generally as referencenumeral 100. We show certain items in block-diagram form more for beingable to reference something consistent with the nature of a patent thanto imply that a certain component is or is not part of a certain device.Similarly, although some items are depicted in the singular form, pluralitems are contemplated as well (e.g., what is shown as one data storemight really be multiple data-stores distributed across multiplelocations). But showing every variation of each item might obscure theinvention. Thus for readability, we show and reference items in thesingular (while fully contemplating, where applicable, the plural).

As shown in FIG. 1A, environment 100 includes one or more sensors 116.In one embodiment, sensors 116 include one or more transducers or typesof sensors operable for providing electrical signals corresponding tomeasurements of various conditions, states, of movements of a user.Embodiments of sensor 116 may further include a power supply, processor,memory operable for acquiring and storing user-information andprogramming instructions, and communication component for communicatingthe resulting measurements of user-information withprocessing/communications component 130. In some embodiments, thetransducer may be a standard electrode, such as a single-terminalelectrode, or a specialized multi-segment or noise-reduction electrode.

In some embodiments, sensor 116 comprises a stride sensor such as thetelemetry Polar 53+® stride sensor, manufactured by Polar Electro Inc.of Lake Success, N.Y., which in some embodiments may be used with inconjunction with a Polar RS800CX® sports watch with a personal-areanetwork (PAN). In some embodiments, sensor 116,processing/communications component 130, and user feedback interface 118are collocated in a single device which may be worn by the user, whilein other embodiments, sensor 116, processing/communications component130, and user feedback interface 118 may be embodied in separatedevices. For example, in one embodiment, sensor 116 is worn on a user'sshoe or ankle while processing/communication component 130 and userfeedback interface 118 are worn on the wrist like a watch. In anotherembodiment, component 130 and user feedback interface 118 are embodiedon the user's smart phone or mobile device and communicate with sensor116, which is worn by the user. In such embodiments, sensor 116 maycommunicate with component 130 via a wireless communication protocol,such as those of existing so called “personal area network” technologiesemployed by exercise and fitness equipment manufacturers, for examplePolar Electro, Nike, and others.

In an embodiment, sensor 116 may be optimally positioned on the user tomeasure motion. In an embodiment, the accelerometer and gyroscopicchip-sets built into many smart phones may be used as sensor 116. Insuch an embodiment, the smart phone, running a program for determininguser ambulation may monitor user motion, and provide entropically-drivenreminders via a user feedback interface, such as user feedback interface118, described below. Accordingly, in one embodiment, the components ofFIG. 1B are embodied on a smart phone or smart watch, which providesfunctionality for sensor 116, processing/communications component 130,user feedback interface 118, and storage 192.

In some embodiments, multiple sensors 116 may be employed on or aboutthe user. One or more sensors may become compromised, and havingmultiple sensors provides for robustness. For example a watch sensor mayget wet when the user washes his hands and fail to operate as normal,while a second sensor located on the user's ankle or within the user'ssmart phone may remain effective. In some embodiments, multiple sensorsat different locations on the user's body may be employed to obtain moreaccurate or thorough kinetic information. In such embodiments,motion-signals corresponding to motion in a particular direction ormotion may be averaged, or may be weighted or scaled according to thelocation of the sensor. For example, motion signals obtained from asensor located on the user's wrist may be weighted less than motionsignals obtained from a sensor worn on the user's chest.

In some embodiments sensor 116 includes one or more accelerometeric orgyroscopic transducers operable to determine gyroscopic and/oraccelerometer measurement of angular velocity in at least one of 3 axes(pitch, roll, yaw) and acceleration in at least one of 3 axes (X,Y,Z)and to provide motion signals corresponding to this angular velocity oracceleration. For example, in some embodiments, sensor 116 includes oneor more transducers, which can take the form of standard MEMSaccelerometer integrated circuit chips, for obtaining electricalkinematic signals from the individual. In one embodiment, a plurality ofaccelerometer sensors and at least one gyroscope sensor, such as the onemanufactured by InvenSense Inc that is used in the Nintendo Wii™ MotionPlus® device, may be deployed on a wearable fabric elastomeric bandpositioned around the chest. Such an embodiment may be used to eliminateor reduce noise, interference, distortion, or artifacts and improveease-of-use and patient compliance.

In some embodiments, a processor of sensor 116 is operable to controlthe measurements; for example, to read a transducer's output at certainintervals such as 50 times each second; to pre-process or condition thesignal, including applying a threshold, noise-filter, or normalizing theraw user-derived signal; read from or store the user-information inmemory, and communicate the acquired time series of user-informationwith processing/communication component 130. In one embodiment, afloor-threshold is applied such that only movements of a certainmagnitude are acquired and communicated to component 130. For example,it may be desirable in some embodiments not to capture every minusculemotion of the user.

Continuing with FIG. 1A, environment 100 includesprocessing/communication component 130. Exemplary embodiments ofcomponent 130 include one or more processors operable for processingsensor information, such as ambulation measures, and for determiningvariability of activity during time intervals and conditionally andintermittently providing user feedback. Some embodiments of component130 also include a communication module for receiving information fromsensor(s) 116 and, in some embodiments, for communicating results to theuser, health-care provider, back-end decision-support services or otherservices, and a memory (illustratively shown as data store 192, anddescribed below) as for storing received user-information, determinedresults, and programming instructions. Component 130 may worn on theuser's body, such as clipped to a belt, in a holster, on the user'swrist, or around the user's neck, or can be carried by the user, such asin the user's pocket or purse, or may be kept with a close enoughproximity to the user as to communicate with sensor(s) 116. In someembodiments, sensor(s) 116 are housed within or on component 130, asdescribed above.

In some embodiments, component 130 is a smart watch such as the PolarRS800CX® sports watch, manufactured by Polar Electro Inc. of LakeSuccess, N.Y. In some embodiments, component 130 is a smart phonerunning one or more application programs or “apps” for receivinguser-sensor information of ambulatory measures or other physicalactivity, determining the degree of variability of activity duringultradian time intervals, conditionally and intermittently providinguser feedback, and in some embodiments (such as an embodiment operatingin the example operating environment depicted in FIG. 1B) communicatingresults to the user, health care provider or back-end service. In asmart-phone embodiment, component 130 may use the phone's datacommunication functionality for communicating user information to abackend, such as a health care provider or decision-support knowledgeagent, or a backend service such as a software service for recording andsharing user progress. Advantages of a smart phone embodiment includethat it can be more easily be periodically synchronized with datastorage, software applications, and other services, which may be presenton a user's personal computer, a web-based host service, or othercomputational services. Additionally, a smart phone embodiment can moreeasily receive software updates or “app” updates as they becomeavailable. In some embodiments, component 130 may use othercommunication features of the smart phone such as Bluetooth, Wi-Fi, orother personal-area-network—based protocols to communicate with one ormore sensors 116 and in some embodiments, a base station or usercomputer. In certain embodiments, without a smart phone,processing/communications component 130 may nevertheless includefunctionality for communicating with backend services includingperiodically synchronized with data storage, software applications, andother services, and receiving software updates.

In embodiments, processing/communications component 130 supportssoftware services (not shown) including one or more software programs or“apps” executed by the one or more processors of component 130.Embodiments of the software services facilitate receiving user-sensorinformation of ambulatory measures or other physical activity,determining the degree of variability of activity during ultradian timeintervals, and conditionally and intermittently providing user feedback.For example, such software services facilitate implementing the methoddescribed in connection to FIG. 2, below.

In some embodiments software services also facilitate logging resultsincluding information about the frequency and types of remindersprovided, user compliance, the amount of physical activity performed bythe user, and communicating results to the user, health care provider orback-end service. Some embodiments of software services performstatistical software operations, and include statistical calculationpackages such as, in one embodiment, the R system (the R-project forStatistical Computing, which supports R-packages or modules tailored forspecific statistical operations, and which is accessible through theComprehensive R Archive Network (CRAN) at http://cran.r-project.org);R-system modules or packages including the ‘entropy’ package tocalculate entropy of accelerometer information acquired from sensor(s)116, such as one example embodiment “steps-per-15-min” time seriesacquired from a Polar RS800CX® sports watch personal-area network (PAN)equipped with a telemetry Polar 53+® stride sensor. In some embodiments,software services also include the Apache Hadoop and H base framework,or similar frameworks operable for providing a distributed file system,while in some embodiments a suitable operating system is employed.

A smart phone embodying component 130 may be communicatively-coupledwith an additional component for facilitating communication with one ormore sensors 116, for processing user-information, or for storing andcommunicating user results. For example, in one embodiment, component130 is communicatively-coupled to a holster or other componentcontaining a communication module for communicating with one or moresensors 116. Such an embodiment is useful where sensors 116 use acommunication protocol that is not compatible with component 130. Forexample, where sensors communicate using Bluetooth, but component 130 isembodied on non-Bluetooth enabled smart phone, the user may attach aBluetooth module to the smart phone to enable it to communicate withsensors 116. Similarly, where sensors 116 communicate using Zigbee oranother low-rate wireless personal area network platform, a user maycouple a Zigbee-enabled communication module to their smart phone. Inanother example embodiment, a smart phone may be communicatively-coupledwith a base station (not shown) located nearby. In one embodiment, thebase station could be a personal computer connected to a wireless routeror a laptop equipped with RF communication capability such as Wi-Fi orBluetooth. In one embodiment, the base station communicates with backendservices or backend 190 (shown in FIGS. 1B and 1C).

In some embodiments, one or more sensors 116 communicate with othersensors 116 and with component 130 over a wired or wirelesscommunication protocol. In one embodiment, sensor(s) 116 communicateusing Bluetooth, Wi-Fi, Zigbee, or similar protocols. In someembodiments a low-powered communication protocol is desirable in orderto preserve the batter life of the sensor 116. In some embodiments usinga communication protocol having a narrow bandwidth, such as Zigbee,sensors 116 may also include a memory buffer for storing user-derivedinformation until it is communicated to component 130. Sensors 116 mayalso communicate with other sensors 116 or directly with a base station(not shown), in some embodiments.

In one embodiment, component 130 is a computer system comprising thefollowing hardware and firmware components: a 32-bit 48 MHz AT91SAM7S256(ARM7TDMI) main microprocessor with 256 KB flash memory and 64 KB RAM,an 8-bit 4 MHz ATmega48 microcontroller with 4 KB flash memory and 512Bytes RAM, a 26 MHz CSR BlueCore 4 Bluetooth controller with 1 MB flashmemory and 47 KB RAM, and 100×64 pixel LCD matrix display. In oneembodiment, the generation of reminders at at pseudorandom intervals forproviding feedback via user feedback interface 118 (such as hapticreminders and other notices described below in connection to userfeedback interface 188) may be performed using a 54C595 8-bit shiftregister (1-tap, 7-bit LFSR), a 54C86 XOR gate, and two NE555 timerchips, wherein the pseudorandom intervals range from approximately 18min to 126 min, for example. It should be understood that variations inhardware and firmware are contemplated by and within the scope of theinvention, and are provide here for illustrative purposes.

Environment 100 of FIG. 1A also includes user feedback interface 118.Embodiments of 118 facilitate providing information to the user such asthe entropically-driven reminders, other feedback regarding the usersphysical activity, or information related to the users physical activityor operation of the device or components of the device. For example, inone embodiment such information might include a report indicating anamount of physical activity performed by the user over a time interval,a target amount of physical activity that the user desires to achieve,and/or a comparison of the amount of activity the user has performed vs.the target amount. In one embodiment, this information might includecompliance information indicating the number and frequency of theentropically-driven reminders provided to the user and whether the userresponded to the reminders by increasing physical activity, for example.In one embodiment, this information might include an indication thatsensor(s) 116 are detected and communicatively coupled to component 130,for example.

Embodiments of user feedback interface 118 may comprise one or more of aspeaker, alarm, or sound producing component, for generating an alarm,or other audible feedback; a vibrating component for providing haptic(tactile vibration) feedback; a display component for displayingmessages, alerts, notices, or other visual feedback to the user, orsimilar interfaces for providing information to a user. In someembodiments, interface 118 is part of a smart phone or smart watch anduses components of the smart phone or watch to provide feedbackinformation to the user. For example, the display screen, the speaker,and/or the vibrating component of the phone may be used as interface 118in one embodiment. In some embodiments, feedback may be provided in theform of an SMS text message or email, which may be displayed or accessedusing the phone.

In some embodiments, the user feedback may be communicated to backedservices, such as logging or tracking software, or sent to a health careprovider or another family member. In some embodiments, such asembodiments where various types of feedback-reminders are provided tothe user (e.g., alarm, text message, playing back a voice recording,vibrating, or similar feedback), component 130 or component 130 inconjunction with services operating on backend 190 (described below inconnection to FIG. 1B), monitor the effectiveness of the feedbackprovided. For example, in some embodiments the type and frequency ofreminders or other feedback is logged as well as subsequent level ofphysical activity performed by the user so that the effectiveness oftypes and timings of reminders for that particular user can bedetermined. Software services, such as decision-support knowledge agentsoftware operating on backend 190, may then analyze the user patternsand respond accordingly, such as by adapting the types and frequency ofresponses provided by the user, modifying the user's targeted activitylevel, or preparing the information for reporting to a health careprovider or insurance provider. In so doing these embodiments can learnto be more responsive to the user.

Turning briefly to FIG. 1B, an illustrative operating environment 101 isshown that includes the components of environment 100 of FIG. 1A, butalso includes backend 190. In some embodiments, component 130communicates directly with backend 190. Embodiments of backend 190 caninclude health care provider computer systems and services,case-management software, electronic health record decision-supportsystems and services, and consumer personal health record systems andservices, for example. In some embodiments, backend 190 includessoftware functionality enabling it to perform as a server, whilecomponent 130 includes software functionality enabling it to perform asthe client.

Referring now to FIGS. 1A and 1B, in some embodiments, component 130stores information on data store 192, which may be local or remotelylocated, and which may be accessible by backend 190, in someembodiments. In some embodiments, data store 192 comprises networkedstorage or distributed storage including storage on servers located inthe cloud. Thus, it is contemplated that for some embodiments, theinformation stored in data store 192 is not stored in the same physicallocation. For example, in one embodiment, one part of data store 110includes one or more USB thumb drives or similar portable data storagemedia. Additionally, information stored in data store 192 can besearched, queried, analyzed via services operating on backend 190, suchas by a health care provider or by a decision-support knowledge agent,for example.

Turning now to FIG. 1C, aspects of an illustrative operating environmentsuitable for practicing an embodiment of the present invention areprovided and referred to generally as environment 102. As shown in FIG.1C, an embodiment of processing/communication component 130 iscommunicatively coupled to wearable motion sensor 112, which is oneembodiment of sensor 116, and docking station 120. In the embodimentshown in FIG. 1C, docking station 120 recharges a battery in component130 and in sensor 112. In some embodiments, docking station 120 maycomprise 2 docking or recharging stations: one for component 130 and onefor sensor 112. Component 130 is communicatively coupled to backend 190,and data store 192, which are described previously in connection toFIGS. 1A and 1B.

In environment 102 of FIG. 1C, motion sensor 112 includes one or moreaccelerometers or gyroscopic transducers. In this embodiment, thetransducers are coupled to an instrumentation operational amplifier, ananalog filter, an analog-to-digital converter, and a Bluetooth orsimilar RF communication component, thereby enabling motion sensor 112,when positioned on the user, to obtain raw motion signals of the user,capture and digitize the raw motion signals, and communicate thisinformation to component 130. Motion sensor 112 also includes a powersupply made up of a battery and multiple-output supply converter.

In the embodiment shown in FIG. 1B, component 130 includes a Bluetoothor similar RF communication component operable to receiveuser-information from motion sensor 112 or from other sensors 116,preprocessing and filtering components operable to condition and formatthe received user information for determining the degree of variabilityof activity during ultradian time intervals and conditionally andintermittently providing user feedback as described in connection toFIG. 2, below, and in some embodiments communicating results to theuser, health care provider or back-end service. Embodiments of component130 shown in FIG. 1C may also include a Bluetooth, cell-phone, or Wi-Ficommunication component for communicating information to backend 190,data store 192, and/or user feedback interface 118 (not shown), whichmay be embodied within the same device as component 130, such as in theform of an alarm or display for providing entropically-driven reminders,diagnostic feedback, power levels, and other information to a user orfor receiving inputs from a user such as parameters and device settings,as described above in connection to FIG. 1A. Embodiments of component130 may also include memory for storing parameters, settings, firmwareand programming instructions, and determined results. Embodiments ofcomponent 130 may also include a power supply which in one embodimentcomprises a battery and a battery balance circuit. In one embodiment,component 130 is a computer system with one or more processors, memory,and input/output functionality.

Turning now to FIG. 2, a flow diagram is provided illustrating anexemplary method 200 according to one embodiment. At a high level, aflow diagram illustratively depicts a method 200 for promoting positiveactivity patterns for users and facilitate long-term adherence to theactivity patterns, by providing entropically-driven activity remindersto ambulate in a fashion that is responsive to a user's actual activitypatterns and behaviors and compatible with routine activities in theworkplace and home. In embodiments, the steps of method 200 are carriedout for a time period r, wherein r represents an ultradian timeinterval, in some embodiments. In particular, in some embodiments r isless than 90 minutes, and preferably is between 10 and 30 minutes,although other time intervals may be used.

In contrast to known methodologies for encouraging individuals to adopthealthy activity patterns, embodiments of present invention do not focusas much on energy balance but instead aim to establish patterns offrequent ambulation at irregular time intervals resembling high-entropypatterns that characterize a subsistence “Hunter-Gatherer” life-style.With continuing reference to FIG. 2, in some embodiments, the entropy ofa time series signal (such as “steps-per-15-min” or other time seriescaptured from MEMS accelerometer chips or other sensor(s) 116, such asare routinely part of smart phones, sports watches, and other wearablemobile devices) is continuously calculated and used to triggerpersonalized, context-adaptive electronic reminders for users wearing orholding devices. Narrow statistical distributions of step counts havelow entropy (contain little information); wide Hunter-Gatherer-typedistributions have high entropy (contain more information) and areassociated with beneficial changes in endocrine systems and dynamicgene-expression regulation.

It has been proposed that relatively low intensity exercise inducesmoderate growth hormone (Gil) responses through activation of thecentral cholinergic system, resulting in a reduction in hypothalamicsomatostatin release. However, it appears that there is an upper limitto this process and at higher exercise intensities, once hypothalamicsomatostatinergic tone is completely suppressed, further increases in GHrelease must be mediated by an increase in growth hormone releasinghormone (GHRH) secretion. The inhibition of somatostatinergic tone as aresult of brief bouts of exercise is accompanied by suppression of theGH response to treadmill exercise, at 60% VO2max, following pretreatmentwith the somatostatin analogue octreocide, in humans. In contrast,administration of the somatostatin inhibitor, pyridostigmine, enhancesactivity-induced GH release, suggesting that pyridostigmine and exercisemight act independently in eliciting the GH response to exercise.Besides GH and GHRH effects, physical activity induces significantincreases of IL-6, cortisol, and leukocytes in comparison to thelow-intensity protocols. Non-exercise physical activity thennogenesis(NEAT) is also important in net daily energy balance and neuroendocrinefunction.

Lifestyle-related diseases are rapidly increasing at least in part dueto less physical activity. Duration, intensity, and frequency have beenutilized by current state of the art methodologies to guide healthrecommendations, but not measures of the ultradian variability ofactivity. Context-sensitive electronic reminders to ambulate (low-dosenon-exercise activity) frequently and at varied, irregular intervalsduring each day constitute a “minimum movement entropy dose” as part ofa comprehensive exercise prescription. Exceeding a minimum entropythreshold is necessary, but is not by itself sufficient for health. Theconventional “calorie intake not exceeding the calories expended” energybalance exercise prescriptions continue to be valid and valuable.However, energy balance-based exercise prescriptions may not beeffective. Even if overtraining or other complications do not arise, adaily regimen of vigorous exercise may reach a point of diminishingreturns. Activity-induced change in body weight obscures the largeinter-individual variability in body weight and compensatory responses.Compensatory changes in appetite and metabolism tend to neutralize thebeneficial effects of structured exercise regimens. Individuals whoexperience a plateau or lower-than-predicted weight loss tend toexperience strong compensatory physiologic and behavioral changes thateffectively offset the net increase in energy expenditure.

The effect of increased unstructured activity need not be weight lossnecessarily but increased health and fitness at a stable weight, as hasbeen emphasized recently by the Health At Every Size (HAES) movement.Instead, a more highly-varied (higher entropy) pattern of frequentambulation and other light activity may be sufficient to promote healthgoals including fitness, weight loss, maintain non-increasing weight.Within broad limits, the higher the entropy of the activity time series,the better (with regard to weight loss and other health goals). Entropyvalues greater than 2.1 are preferred. Values less than 1.2 suggestinsufficient variability to induce the alterations in gene expressionand other adaptations in muscle and other tissues that are associatedwith improvements in metabolism.

The health beneficial effects of regular physical activity includemetabolic adaptations in skeletal muscle, which are thought to beelicited by cumulative effects of transient gene responses to each boutof exercise, but how is this regulated? A potential candidate in this isthe transcriptional co-activator peroxisome proliferator-activatedreceptor-gamma co-activator (PGC)-1alpha, which has been identified as amaster regulator of mitochondrial biogenesis, but also been shown toregulate proteins involved in angiogenesis and the anti-oxidant defenseas well as to affect expression of inflammatory markers. Exerciseincreases PGC-1alpha transcription and potentially PGC-1alpha activitythrough post-translational modifications, and concomitantPGC-1alpha-mediated gene regulation is suggested to be an underlyingmechanism for adaptations in skeletal muscle, when exercise is repeated.The current review presents some of the key findings inPGC-1alpha-mediated regulation of metabolically related, anti-oxidantand inflammatory proteins in skeletal muscle in the basal state and inresponse to exercise training, and describes functional significance ofPGC1 alpha-mediated effects in skeletal muscle. In addition, regulationof PGC-1alpha expression and activity in skeletal muscle is described.The impact of changes in PGC-1alpha expression in mouse skeletal muscleand the ability of PGC-1alpha to regulate multiple pathways andfunctions underline the potential importance of PGC-1alpha in skeletalmuscle adaptations in humans. The absence of exercise-inducedPGC-1alpha-mediated gene regulation during a physical inactive lifestyleis suggested to lead to reduced oxidative capacity of skeletal muscleand concomitant impaired metabolism. PGC-1alpha overexpression causedhepatic insulin resistance, manifested by higher glucose production anddiminished insulin suppression of gluconeogenesis. Paradoxically,PGC-1alpha overexpression improves muscle insulin sensitivity, asevidenced by elevated insulin-stimulated Akt phosphorylation andperipheral glucose disposal. PGC-1insufficiency may predispose towardPAD or exacerbate its severity or rate of progression or both.

Even though most of these pathways are activated by different stimuliand in a temporally and spatially separated manner during exercise, manyof the major signal transduction events converge on the peroxisomeproliferator-activated receptor γ co-activator 1α (PGC-1α) bypost-translationally modifying the PGC-1α protein, modulating PGC-1αgene expression or both. In turn, depending on the cellular context,PGC-1α regulates specific gene programs. Ultimately, PGC-1α modulatesmost of the transcriptional adaptations of skeletal muscle to exercise.Emerging evidence suggests that normal physiological adaptations to aheavy lipid load depend on the coordinated actions of broadtranscriptional regulators such as the peroxisome proliferator activatedreceptors (PPARs) and PPAR gamma co-activator 1 alpha (PGC1 alpha). Theapplication of transcriptomics and targeted metabolic profiling toolsbased on mass spectrometry has led to our finding that lipid-inducedinsulin resistance is a condition in which up-regulation ofPPAR-targeted genes and high rates of beta-oxidation are not supportedby a commensurate up-regulation of tricarboxylic acid (TCA) cycleactivity. In contrast, exercise training enhances mitochondrialperformance, favoring tighter coupling between beta-oxidation and theTCA cycle, and concomitantly restores insulin sensitivity in animals feda chronic high-fat diet. The exercise-activated transcriptionalco-activator, PGC1 alpha, plays a key role in coordinating metabolicflux through these two intersecting metabolic pathways, and itssuppression by overfeeding may contribute to diet-induced mitochondrialdysfunction. Our emerging model predicts that muscle insulin resistancearises from a mitochondrial disconnect between beta-oxidation and TCAcycle activity. Understanding of this “disconnect” and its molecularbasis may lead to new therapeutic approaches to preventing and managingobesity and metabolic diseases such as Type-2 diabetes.

It is known that transcription of the PGC-1α gene can be eitherinhibited or stimulated by p38 MAP kinase (p38 MAPK). Furthermore, basalAkt-dependent signaling is affected by PGC-1α expression levels. The p38MAPK-induced PGC-1α gene transcription is prevented by insulin. Insulinsuppression of PGC-1α gene transcription is in turn neutralized byoverexpression of the FoxO1 gene. Essentially, transcription of thePGC-1α gene is balanced by different intracellular signaling pathways,which affect a diverse metabolic and vascular physiologic pathways.

Endurance exercise is known to induce metabolic adaptations in skeletalmuscle via activation of the transcriptional co-activator peroxisomeproliferator-activated receptor γ co-activator 1α (PGC-1α). PGC-1αregulates mitochondrial biogenesis via regulating transcription ofnuclear-encoded mitochondrial genes. In response to acute altered energydemands, PGC-1α re-localizes into nuclear and mitochondrial compartmentswhere it functions as a transcriptional co-activator for both nuclearand mitochondrial DNA transcription factors. These results suggest thatPGC-1α may dynamically facilitate nuclear-mitochondrial DNA crosstalk topromote net mitochondrial biogenesis, which in turn affects the rate atwhich the body metabolizes nutrients and utilizes energy.

PGC-1α is a transcriptional co-activator that powerfully regulates manypathways linked to energy homeostasis. Specifically, PGC-1α controlsmitochondrial biogenesis in most tissues but also initiates importanttissue-specific functions, including fiber type switching in skeletalmuscle and gluconeogenesis and fatty acid oxidation in the liver. S6kinase, activated in the liver upon feeding, can phosphorylate PGC-1αdirectly on two sites within its arginine/serine-rich (RS) domain. Thisphosphorylation significantly attenuates the ability of PGC-1α to turnon genes of gluconeogenesis in hepatocytes, while leaving the functionsof PGC-1α as an activator of mitochondrial and fatty acid oxidationgenes completely intact. These phosphorylations interfere with theability of PGC-1α to bind to HNF4a, a transcription factor required forgluconeogenesis, while leaving undisturbed the interactions of PGC-1αwith ERRa and PPARa, factors important for mitochondrial biogenesis andfatty acid oxidation. Experimental data reveal that S6 kinase can modifyPGC-1α, providing metabolic flexibility needed for dietary adaptation.

The network of inter-related up- and down-regulation of PGC-1α—and othergenes involved in energy/metabolism pathways and mitochondrialbiogenesis and capacity for insulin sensitivity and glucose andfatty-acid oxidation such as AMPK, p38 MAPK, CaMKII, Akt/PKB, mTOR, S6K,p70S6k, 4E-BP1, MKK3 and MKK6, CREB, ATF-2, HDAC4, MEF2, STARS,adiponectin, SIRT3, TNF-alpha, and HIF-2alpha—occurs on an ultradiantimescale (5 min to 180 min). Several hours after completing a bout ofexercise, the concentrations of gene products and gene expression levelshave reverted to baseline levels. This possibly explains why 30 min ofmoderate to vigorous exercise once per day may not achieve the intendedeffect in terms of weight loss, diabetes prevention, or other goals.Instead, a more highly varied pattern of intermittent light activityoccurring at irregular intervals throughout the day may result ingene-expression and other physiologic adaptations that more consistentlyimprove metabolism and fitness, despite a comparatively small averagedaily exercise dose.

The current state of the art for activity reminding and exerciseprescribing has several limitations, including:

(1) The measurement and analytics methods address only circadian orweekly or other longer timescale patterns, and do not address ultradian(short timescale) patterns' relationship to fitness, which may providenumerous advantages, as indicated in the discussion of the precedingparagraphs.

(2) The underlying principles are solely based on energyintake/expenditure balance, and do not take into accountactivity-induced up- and down-regulation of the expression of certainmetabolism-related genes such as PGC-1α that are related to muscle andorgan system metabolism, cardiovascular function, and functioning ofendocrine organs and receptors.

(3) The reminders or recommendations are emitted at precise, scheduledintervals, often without adequate (or any) personalization or reflectionof recent user actions and behavior. Standardized, routinized messagesare overly regimented or intrusive. After a short period of use thereminders become perceived as “nagging” or “boring”, such that mostusers soon opt to ignore the advice or cease using the device andsoftware application.

(4) Existing static, overly-simplistic models lack an adequateabstraction of user psychology, and the motivations for accepting andadhering to, vs. rejecting, coaching recommendations.

Accordingly, it is therefore highly valuable and highly desirable toprovide embodiments of the methods and systems described herein, formitigating the aforementioned limitations, particularly embodiments ofthe invention that are also not susceptible to biases, that are tolerantof modest amounts of missing or sensing-artifact contaminated values ofmodel-variables information, that take advantage of longitudinal trendsand physiological causation of trends, and that do not require extensiveor intrusive questioning or detailed self-reported information frompatients. Moreover, excellent adherence to physical activityprescriptions is essential for the successful prevention or managementof obesity, hypertension, osteoporosis loss of bone mineral density,sarcopenia (weakness and muscle loss), and falls risk.

Continuing with reference to FIG. 2, several presently available healthpromotion wearable monitors (such as Jawbone's UP® wrist band device)include an option to request periodic activity reminders, usually by aninaudible vibration signal that is sensible on the skin near thewearable monitoring device. But as previously described, if suchactivity reminders are periodic (intensive reminders, always emitted atthe same interval) then a user is likely to fall into a pattern ofnoncompliance. Over time, the repeated reminding at a fixed rate isignored at a progressively higher percentage of the time. The monitoringdevice is viewed as a “nag” and one that can be ignored with impunity,so that the reminding is ineffective in longterm chronic use.

If, on the other hand, activity reminders are (a) varied andinteresting, emitted at irregular intervals, and (b)situationally/contextually sensitive, emitted only at times when thehistorical pattern of activity indicates that the user has beenexcessively sedentary (as is true of the present invention), thenadherence/compliance is sustained indefinitely in a higher percentage ofpeople, and there is a greater probability of achieving the user'shealth goals.

With particular reference to activity reminders that aresituationally/contextually sensitive, in one exemplary embodiment of theinvention, a prototype was implemented with a linear feedback shiftregister (LFSR) pseudo-random interval generator being conditionallytriggered by entropy values that were below a target threshold. Inoperation, the entropy-triggered LFSR emitted only about 36% as manyreminders per day, compared to another embodiment (an early prototypeversion) that utilized always-on LFSR random triggering. It is possiblethat compliance with a high-entropy, hunter-gatherer-type pattern ofactivity is enhanced in part due to the user's learned behaviors thatinvolve more frequent spontaneous initiation of ambulation at irregularintervals, which has the consequence of avoiding low-entropy triggeringof reminders. In this way, the user's adoption of a behavioral changethat entails moderate spontaneous movement with spectral peaks coveringa wide range of frequencies is rewarded by the total cessation ofreminders. Embodiments of the monitoring device then are viewed as a“coach”—an intelligent and fair agent whose decisions exhibitattentiveness and factual insight into the user's own actions—and not asa “nag”. Accordingly, the artificial intelligence exhibited by theseembodiments emulates a sort of social support, characterized by haptic‘presence’ that is predominantly affirmative: silent when spontaneousbehaviors are conforming to goals and reinforcing incrementalimprovements, and only intervening or intruding when a necessarycorrective action is factually indicated.

Additionally, implementation of the prototype has shown that theduration of the activity reminder (such as a haptic tactile vibrationalert on a wrist band, or other embodiments of user feedback interface118, as described above in connection to FIGS. 1A and 1B) was stronglycorrelated with compliance rate. Reminders that lasted less than 10seconds were complied with only 24% of the time, while reminders of 30sec duration or more were associated with 91% compliance.

Health researchers have found that psychologically well-suited socialsupport and reminders are related to positive health outcomes across abroad range of disease contexts, for example diabetes and heart disease.Reminding patterns can include (1) regular reminding that was habituallyoffered, (2) situational reminding adapted to changing circumstances orbehaviors, and (3) intensive reminding, either regular (i.e., nagging)or situationally varied. Instrumental helping involves monitoringactivity and measuring whether behavior is trending in favorable orunfavorable directions. Coaching involves context-adaptive reminding,shaping behavior by reinforcing incremental gains and offering a varietyof affirmation messages (e.g., “It has been more than 72 hours since Ihad to remind you to get up and walk around. Great work!” or shorter“SMS language” or yet shorter symbolic emoticons).

Social support researchers have wrestled with a contradiction thatresearch in this area has produced, namely, perceived social supportcorrelates with a range of positive physical and psychological outcomes,but received social support very often is associated with negativeimpacts on well-being. Some researchers have explored ways to resolvethis contradiction. They suggest that being the recipient of socialsupport in times of stress could lower self-esteem because it highlightsthe support recipient's inability to manage the situation without help.One proposal is that some effective support might be “invisible,” thatis, the support provider could describe the actual support given, butthe support recipient would not have perceived it. In embodiments of thepresent invention, monitoring the user's ambulation has an “invisible”component in that if the user had spontaneously ambulated, thesupportive partner did not need to point it out to the partner. Coachingalso had an “invisible” aspect to it in that it came in the form ofsuggestions, interactive exchange, and/or congratulations/affirmations.Also, regular reminding had become an ingrained habit and as with mosthabits, is after a time not particularly noticed. Effective remindingrequires dynamic adaptation and responsiveness to actual behavior.

Still with reference to FIG. 2, as described above, in some embodiments,the steps of method 200 are carried out for a time period τ, which mayrepresent ultradian time interval. At a step 210, user-motioninformation is obtained. In embodiments, user motion information isreceived from sensor(s) 116 and may comprise measured ambulation orother physical activity measured by sensor(s) 116. In one embodiment,step 210 includes measuring and recording ambulation from signalsprovided by a wearable accelerometer sensor 116 in the form of usersteps (or units of movement). The steps (or units of movement) aredetected, counted and summed in memory.

At a step 220, a time series is determined for the current time periodτ. In embodiments, the information obtained in step 210 (for example,the counted and summed steps (or units of motion) for the current timeperiod) are deposited into a time series. The time series may beimplemented in any number of was as are known in the art. In someembodiments, the time series includes summed steps for only a limitednumber of recent time intervals, such as for example time intervals overthe previous 24 hour period, the previous 3 days, week, or other timespan. In some embodiments, all step- or unit-motion-count information isstored in a record so that it can be accessed at a later time.

In one embodiment, the time series is implemented using a circularbuffer 1-dimmensional array. In one embodiment, the array contains1440/r bins, wherein each bin's value is the total number of steps (orunits of motion) counted in a τ-min time interval. (In this embodiment,1440 represents the number of minutes in a 24 hour period.) In oneembodiment, τ is preferably between 10 and 30 minutes. In someembodiments using the circular buffer, to enable step-counts for acurrent r interval that is commencing to be accumulated, the cell at thetail-end of the buffer may be zeroed out or cleared, and all values inthe buffer shifted leftward one position, thereby updating the buffer.Or alternatively, in those embodiments, the pointer to the head of thecircular buffer may me incremented by one so that it now points toanother bin (which becomes the new head) of the buffer. The time seriesfor the current period is then determined by storing the counted steps(or units of motion) for the current time period into the bin indicatedby the pointer (i.e. the current head of the buffer).

At a step 230, a quantitative measure of variability based on thecurrent time series is determined. In embodiments the measure ofvariability comprises a Shannon, Jeffreys, Laplace, Minimax, Tsallis,Chao-Shen, Dirichlet, Miller-Madow, James-Stein Shrinker, orNemenman-Shafee-Bialek (NSB) entropy, or other measures of variabilitysuch as RMSSD or other measures such as are known to those practiced inthe art. In one embodiment for the quantitative measure of variabilitydetermination, the entropy may be expressed as:

$H = {- {\sum\limits_{i = 1}^{N}\left( {{p\left( a_{i} \right)}*{\log\left( {p\left( a_{i} \right)} \right)}} \right)}}$In some embodiments, as the entropy level (or other quantitative measureof variability) is determined for each time interval r, that determinedentropy level is stored in data store 192 such that it and the entropylevels determined for previous time intervals are accessible bycomponent 130, so that analysis may be performed based on, for example,comparing most recently determined entropy level with the entropy levelof the previous time interval, or entropy level of a particular pasttime interval. In embodiments, the entropy level determined for currenttime series may be referred to as the “current entropy” (or “currententropy level”); the entropy level determined for the previous timeinterval referred to as the “previous entropy.” Accordingly, inembodiments, with each new time interval and determination of entropylevel corresponding to that time interval, the value of the “currententropy level” will become the value of the “previous entropy level.”

At a step 240, a target threshold is determined. In embodiments, thetarget threshold corresponds to an amount of physical activity desiredto be performed by the user (usually a minimum amount of activitydesired, although in some embodiments, such as where a user desires toavoid or minimize physical activity, this threshold could represent amaximum). Thus, embodiments of this step determine the value thatcorresponds to a desired variability level for comparing (in a step 250)with the quantitative measure of variability determined in step 230. Inone embodiment, the target threshold is a value that corresponds to agiven amount of physical activity during the time interval, such asminutes of movement or ambulation, units of motion, number steps taken,or similar measure. In some embodiments, this target threshold value isset or provided by the user or health care provider, and in someembodiments, the target threshold is adaptive, for example, it mayincrement as the user's fitness improves. In some embodiments, the valueis based on the amount physical activity recently performed by the user,or typically performed by the user over recent days, weeks, or months.For example, a “training program” feature of software services operatingon component 130 may incrementally adjust the threshold in order tomotivate the user to continue to increase the level of physical activityperformed. In some embodiments, the threshold is provided by the uservia a user interface, which in some embodiments may be part of userfeedback interface 118. In one embodiment, a software program such as an“app” running on component 130, has a setting wherein a user may specifya desired minimum amount of physical activity (such as number or stepsor minutes of ambulation, or other units of motion or physicalactivity). Similarly, the app may also provide the user with an optionto specify the ultradian time interval τ. In one embodiment a healthcare provider may specify the threshold (or τ, or both) via a softwareservice on backend 190.

At a step 250, a comparison is performed between the current entropylevel and a target threshold. In embodiments, a current entropy levelwhich exceeds the threshold suggests that the user has at leastperformed a minimum desired level of physical activity over the timeinterval τ. Accordingly, where the entropy level (or other quantitativemeasure of variability) or measure of physical activity over the timeinterval exceeds the target threshold, then the method proceeds to step255 and quiesces until the current time period has elapsed. On the otherhand, a current entropy level which does not exceed the threshold isindicative of the user not having performed the minimum desired level ofphysical activity over the time interval τ. Thus, where the targetthreshold exceeds the entropy level (or other quantitative measure ofvariability) or measure of physical activity over the time interval,then the method proceeds to step 260, in some embodiments. In someembodiments, the result of the comparison of step 250 is recorded orlogged, so that for example a user or health care provider can look backand determine how many time intervals the desired minimum activity wasachieved or not achieved. In some embodiments a counter or log is keptof intervals where the threshold was and/or was not exceeded. Suchcounter or log may be referenced to determine additional criteria instep 260, in some embodiments. For example, one criterion might bewhether the target threshold exceed the previous entropy level, thus inthis embodiment, a log of recent comparisons of step 250 might beaccessed to determine the outcome of the previous comparison. Additionalcriteria are described below in connection step 260.

In some embodiments, where the target threshold exceeds the entropylevel other measure over the time interval, then the method skips step260 and proceeds to step 270. It is also contemplated that in someembodiments where the target threshold exceeds the entropy level othermeasure, the method proceeds directly to step 290 where an activityreminder is emitted.

At step 260, it is determined whether an additional criterion (orcriteria) are satisfied in order to potentially emit a reminder to theuser. For example, in some embodiments, method 200 only proceeds to step270 if a second criterion is also satisfied (wherein the first criterionrefers to the target threshold exceeding the entropy level). Inembodiments, where the one or more criteria are satisfied in step 260,the method proceeds to step 270; but where the one or more criteria arenot satisfied, the method proceeds to step 255. Examples of anadditional criterion (or criteria) to be satisfied include, for example,that the entropy level determined for the previous time interval (theprevious entropy level) was also determined to be below the targetthreshold. (This implies that the user has failed to achieve a minimumlevel of ambulation or physical activity for two consecutive timeintervals.) In another example criterion, the threshold has exceeded theentropy level for at least a specified number of intervals over the past24 hours, such as for example at least eighty percent of the intervals.In another example, the criterion is that it has been greater than acertain amount of time, such as 8 hours since a reminder was emitted. Insome embodiments, multiple criteria may be evaluated at step 260, suchthat all criteria may be to be satisfied, or any one of a set ofcriteria satisfied. For embodiments which include step 260, specificadditional criteria utilized should be considered in light of the abovediscussion regarding effectiveness of the activity reminder so as not toresult in overly reminding (“nagging”) the user nor to so rarely remindthe user (when the user is not performing a desired level of activity)as to be ineffective.

At a step 270, a pseudorandom function is invoked. Embodiments of thisstep use a pseudorandom function to provide an output that is eitherlogically true of false. In embodiments, a purpose for this step is toprovide a degree of randomness regarding whether or not to emit anactivity reminder to a user, when conditions are present that warrantthe user receiving such a reminder (e.g., the current entropy level isbelow the target threshold and an additional criteria is satisfied (suchas the previous entropy level also being below the target threshold).Thus, even where the user is performing in a manner that “needs’ anactivity reminder, an activity reminder is not necessarily provided. Inembodiments, this results in the activity reminders being emitted atnon-regularly occurring times. In one embodiment, a linear feedbackshift register (LFSR) and an XOR gate (such as a 54C86 XOR gate) areused to provide a pseudorandom output. For example, in this embodiment,applying steps 250 and 260, if entropy level is below the targetthreshold and the r interval just completed was the second suchbelow-entropy-threshold interval, then at step 270 enable the clocksignal input to the pseudo-random interval generator (i.e. the LFSR andone-tap XOR gate). In one embodiment, the pseudorandom function isprovided by a software function.

In some embodiments, the output of the pseudorandom function may beweighted such that, although still random, the output is biased to belogically true or false for a certain percentage of time other thanfifty percent. For example in one embodiment, the output is “true”twenty-five percent of the time and the output is “false” seventy-fivepercent of the time. This embodiment of step 270 may be used in someembodiments where the method 200 does not include step 260. Thus, havinga pseudorandom function output true (and therefore result in an emissionof a reminder) less than fifty percent of the time, may provideentropically-driven reminders at a frequency not likely to annoy or bedismissed by the user.

At a step 280, based on the outcome of the pseudorandom function of step270, the method proceeds to step 290 or to step 255. For example, wherethe pseudorandom output is a first value corresponding to logically true(or “high” or 1), then the method goes to step 290. But where the outputis a value corresponding to logically false (or “low” or 0), then themethod proceeds to step 255. In one embodiment using the LFSR and XORgate, if the output of the pseudo-random generator's XOR gate goes to alogical-high “true” value, then proceed to step 290 and emit an activityreminder signal to the human user.

At a step 290, an activity reminder is emitted. Although the term“activity reminder” is used, it is contemplated that any variety ofnotices or other feedback may be emitted in embodiments at step 290including, for example, the various types of feedback described above inconnection with user-feedback interface 118. For example, in someembodiments, a reminder may take the form of an alarm, music, or voicerecording, or other audible feedback, haptic feedback, displaying amessage, alert, notice, or other visual feedback provided to the user,an SMS message or email, which may be provided to the user, a healthcare provider, or another person such as a family member. In someembodiments, the type of reminder emitted may be specified by the useror healthcare provider (for example in a user-preferences feature ofsoftware services operating on component 130). In some embodiments, thetype of reminder is determined based on the types of previously emittedreminders, and in some embodiments based further on the effectiveness ofthose reminders. Thus in some embodiments, the types of reminder may bevaried or repeated based on the effectiveness of previously emittedtypes reminders, as determined by component 130 (or component 130operating in conjunction with services on backend 190) as describedabove in connection with component 130. For example, in someembodiments, the reminder types are varied. While In some embodiments,previous reminder types that were effective may be used again, and whereprevious reminder types were ineffective, another type of reminder maybe used. In one embodiment, effectiveness of the reminder and remindertype is based on whether the entropy level for the time intervalfollowing the emission of the reminder exceeds the target threshold. Inanother embodiment, effectiveness is based on whether the entropy levelfor the time interval following the emission of the reminder is greaterthan the entropy level for the time interval for which the reminder wasissued. (In other words, the user increased ambulation or physicalactivity, even if it was not enough to exceed the target threshold.) Insome embodiments, the type of reminder emitted and the time of thereminder is recorded.

At a step 255, the method enters a quiescent state for the remainder oftime interval. In this state, no reminders are emitted for the remainderof the time interval either because the user has achieved sufficientambulation or the outcome of the pseudorandom function (step 270) wasfalse, 0, or a logical low, which results in no reminder being issued,in some embodiments. In some embodiments, after step 255, or 270, themethod 200 returns to step 210. In some embodiments, at the end of timeinterval τ, method 200 returns to step 210. In some embodiments, method200 is performed for ultridian time intervals throughout a 24 hourperiod, or throughout a user's waking day.

In some embodiments, a device comprising sensor(s) 116, component 130executing computer instructions embodying method 200, and user feedbackinterface 118, acquires baseline “steps-per-15-min” counts time seriesduring the user's initial use of the device. During the initial periodwhile much of the system's time series memory contains zero ordefault/null entries, the entropy is sub target-threshold and the methodalways proceeds to step 260 and/or 270 (in embodiments with the LFSR,the LFSR is “on”), resulting in reminders emitted at random intervals.The device's action during this initial period thus resembles that of acoach or teacher who has just acquired a new student. Later, after thetime series buffer memory is filled with actual user activity timeseries data (typically several thousand steps, accrued over the previous24 hours), if the entropy exceeds the target threshold then the methodproceeds to step 255 (in embodiments with an LFSR, the LFSR is disabled)and reminders are suppressed. Again, this action closely mimics that ofa coach or teacher who attentively, but “invisibly”, monitors a studentwith whom s/he is already familiar and whose performance conforms to thetraining plan. Thus in some embodiments of the invention, a prolongedpattern of non-adherence generates a correspondingly prolonged period ofintensive reminding, until the entropy of the “boxcar” buffer memorycontaining the steps-per-15-min time series once again ascends above thetarget threshold.

Reminding that is routinized, periodic, ongoing habitual interactionbecomes boring or intrusive, and adherence rapidly declines. Bycontrast, this coach-like, time-limited, carefully targeted,entropy-triggered reminding pattern of haptic or other feedback achieveshigher levels of adherence that are sustained over prolonged intervals.Such a reminding system and method can become a part of a comprehensivesuite of strategic psychological support practices, to promote andeffectively achieve health goals.

Example embodiments of the invention were evaluated on users and shownto be effective. Participants were instructed to wear a deviceembodiment of the invention during day time, except during bathing.Participants who did not manage to record 500 min/d of activity forthree d were excluded from further analyses. Zero-activity periods of 10minutes were interpreted as “not-worn” time intervals, and these periodswere removed from the summation of activity. Weight loss over a 3-monthperiod averaged 14% for overweight subjects.

With continuing reference to FIGS. 1A through 2, additional exampleembodiments include:

A computer-implemented method for accelerometry-based logging ofambulation and other physical activity utilizing sensor data acquisitionand a data processing system, comprising: (a) determining the degree ofvariability of activity during ultradian time intervals, preferably lessthan 90 min and more preferably between 10 min and 30 min; and (b)conditionally and intermittently emitting a reminder signal inreal-time, and involving one or more of: a haptic (tactile vibration)signal, sensible on the skin of the user in the area where the wearablemonitoring device is affixed; an audible signal; and a supportive oraffirmative textual or symbolic message—on a display that is part of thedevice itself, or on a mobile phone or other wireless communicationdevice carried by the user.

In some embodiments of the computer-implemented method, a 24-hour bufferof stored accelerometry timeseries data is continuously characterized bya quantitative measure of variability, comprising a Shannon, Jeffreys,Laplace, Minimax, Tsallis, Chao-Shen, Dirichlet, Miller-Madow,James-Stein Shrinker, or Nemenman-Shafee-Bialek (NSB) entropy, or othermeasures of variability such as RMSSD or other measures such as areknown to those practiced in the art. In some embodiments of thecomputer-implemented method, the calculation of the variability measureis performed at ultradian intervals, preferably less than 90 min andmore preferably between 10 and 30 min.

In some embodiments of the computer-implemented method, each successivedetermination of low-entropy sedentariness within a specified timeinterval is checked against previous determinations by the method usedfrom the types of methods listed above, and if two consecutivedeterminations are less than a target entropy threshold then that shallbe used as a trigger to cause the generation of an electronic reminderto prompt the user to commence a bout of ambulatory activity or lightexercise.

In some embodiments of the computer-implemented method, the user'sphysical activity (or lack thereof) subsequent to receiving an alert orreminder is ascertained and logged in the device's memory for subsequentanalysis and personalization of future reminders. In some embodiments ofthe computer-implemented method, the reminder signal is emitted for notless than 10 seconds. In some embodiments of the computer-implementedmethod, the monitoring and analysis of activity variability and ofcompliance with the emitted reminders are implemented by periodicallysynchronizing a user worn monitoring device with data storage andsoftware applications that are present on the user's laptop computer, ona web-based host service, or other computational resources.

Although the invention has been described with reference to theembodiments illustrated in the attached drawing figures, it is notedthat substitutions may be made and equivalents employed herein withoutdeparting from the scope of the invention as recited in the claims. Forexample, additional steps may be added and steps omitted withoutdeparting from the scope of the invention.

It will be understood that certain features and subcombinations are ofutility and may be employed without reference to other features andsubcombinations and are contemplated within the scope of the claims. Notall steps listed in the various figures need be carried out in thespecific order described.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the spiritand scope of the present invention. Embodiments of the invention havebeen described with the intent to be illustrative rather thanrestrictive. Alternative embodiments will become apparent to thoseskilled in the art that do not depart from its scope. A skilled artisanmay develop alternative means of implementing the aforementionedimprovements without departing from the scope of the invention.

What is claimed is:
 1. A device comprising one or more motion sensors, a user interface, one or more processors, and one or more memory devices having computer-executable instructions embodied thereon, that, when executed by the one or more processors, implement a method for generating and presenting adaptive activity reminders to a user, via the user interface, for promoting positive activity patterns for the user, the method comprising: by way of the one or more processors, for each of a plurality of contiguous time intervals spanning a first period of time: receiving user motion information during the time interval, wherein the user motion information includes motion measurements which represent an ambulation for the user measured using the one or more motion sensors; determining a time series wherein the time series is based on the motion measurements received during the time interval and based on motion measurements received during previously occurring time intervals within the first period of time; calculating a quantitative measure of an amount of variation in the ambulation for the time interval based on the time series; specifying a target threshold of ambulation variation; comparing the target threshold to the quantitative measure of the amount of variation in the ambulation for the time interval; based on the comparison, determining that the amount of variation in the ambulation is less than the target threshold; based at least on the amount of variation in the ambulation being less than the target threshold, generating a pseudorandom value; and based on the generated pseudorandom value, generating an activity reminder and causing the activity reminder to be presented by way of the user interface on the device.
 2. The device of claim 1, wherein the first time period is twenty-four hours, and wherein each of the plurality of contiguous time intervals comprises an ultradian time interval.
 3. The device of claim 1, wherein the pseudorandom value represents logically true or false, and wherein presenting the activity reminder comprises presenting the activity reminder when the pseudorandom value represents logically true, and not presenting the activity reminder when the pseudorandom value represents logically false.
 4. The device of claim 1, wherein calculating the quantitative measure of the amount of variation in the ambulation comprises determining one of Shannon, Jeffreys, Laplace, Minimax, Tsallis, Chao-Shen, Dirichlet, Miller-Madow, James-Stein Shrinker, or Nemenman-Shafee-Bialek (NSB) entropy, or RMSSD variability.
 5. A system for generating and presenting adaptive activity reminders to a user for promoting positive activity patterns for the user, the system comprising: one or more wearable motion sensors configured to measure user motion; a computing device comprising one or more processors, one or more memory devices, and a user interface, wherein the computing device uses the one or more processors and one or more memories to: receive user motion information over a time interval for each of a plurality of time intervals spanning a first time period, wherein the user motion information includes motion measurements which represent an ambulation for the user measured using the one or more wearable motion sensors; for a first time interval, determine a first time series based on the motion measurements received during the first time interval and the motion measurements received during each time interval of a subset of the plurality of time intervals, the subset of time intervals occurring within the first time period and before the first time interval; calculate a first quantitative measure of an amount of variation in the ambulation for the first time interval, based on the first time series; specify a target threshold of ambulation variation; compare, in a first comparison, the target threshold to the first quantitative measure of the amount of variation in the ambulation; based at least on the first quantitative measure of the amount of variation in the ambulation being less than the target threshold, generate a pseudorandom value by means of a pseudorandom function; weight the output of the pseudorandom function such that the pseudorandom value is biased; and based on the generated pseudorandom value, generate an activity reminder and present the activity reminder by way of the user interface of the computing device.
 6. The system of claim 5, further comprising: for a second time interval immediately following the first time interval, determining a second time series based on the motion measurements received during the second time interval, the motion measurements received during the first time interval, and the motion measurements received for each time interval of the subset of the plurality of time intervals; calculating a second quantitative measure of an amount of variation in the ambulation for the second time interval based on the second time series; and comparing, in a second comparison, the target threshold to the second quantitative measure of the amount of variation in the ambulation; wherein the pseudorandom value is generated based on each of the first quantitative measure of the amount of variation in the ambulation and the second quantitative measure of the amount of variation in the ambulation being less than the target threshold.
 7. The system of claim 5, further comprising: for a second time interval immediately following the first time interval, determining a second time series based on the motion measurements received during the second time interval, the motion measurements received during the first time interval, and the motion measurements received for each time interval of the subset of the plurality of time intervals; calculating a second quantitative measure of an amount of variation in the ambulation for the second time interval based on the second time series; and comparing, in a second comparison, the first quantitative measure of the amount of variation in the ambulation to the second quantitative measure of the amount of variation in the ambulation; wherein the pseudorandom value is generated based on the first quantitative measure of the amount of variation in the ambulation being less than the target threshold and the second quantitative measure of the amount of variation in the ambulation being less than the first quantitative measure of the amount of variation in the ambulation.
 8. The system of claim 5, wherein the first time period is twenty-four hours, and wherein each of the plurality of time intervals spanning the first time period comprises an ultradian time interval.
 9. The system of claim 5, wherein the pseudorandom value represents logically true or false, and wherein presenting the activity reminder comprises presenting the activity reminder when the pseudorandom value represents logically true, and not presenting the activity reminder when the pseudorandom value represents logically false.
 10. A system for generating and presenting adaptive activity reminders to a user for promoting positive activity patterns for the user, the system comprising: one or more wearable motion sensors configured to measure user motion; a computing device comprising one or more processors, one or more memory devices, and a user interface, wherein the computing device uses the one or more processors and one or more memory devices to, for each of a plurality of contiguous time intervals spanning a first period of time: receive user motion information during the time interval, wherein the user motion information includes motion measurements which represent an ambulation for the user measured by the one or more motion sensors worn by the user; determine a time series, wherein the time series is based on the motion measurements received during the time interval and based on motion measurements received during previously occurring time intervals within the first period of time; calculate a quantitative measure of an amount of variation in the ambulation for the time interval based on the time series; specify a target threshold of ambulation variation; compare the target threshold to the quantitative measure of the amount of variation in the ambulation for the time interval; based on the comparison, determine that the amount of variation in the ambulation is less than the target threshold; based at least on the amount of variation in the ambulation being less than the target threshold, generate a pseudorandom value; and based on the generated pseudorandom value, generate an activity reminder and cause the activity reminder to be presented by way of the user interface of the computing device. 